Spaces:
Build error
Build error
File size: 3,332 Bytes
fa6fa48 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import sys
import json
from PIL import Image
import cv2
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, __dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
def main():
global_config = config['Global']
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# sr transform
config['Architecture']["Transform"]['infer_mode'] = True
model = build_model(config['Architecture'])
load_model(config, model)
# create data ops
transforms = []
for op in config['Eval']['dataset']['transforms']:
op_name = list(op)[0]
if 'Label' in op_name:
continue
elif op_name in ['SRResize']:
op[op_name]['infer_mode'] = True
elif op_name == 'KeepKeys':
op[op_name]['keep_keys'] = ['img_lr']
transforms.append(op)
global_config['infer_mode'] = True
ops = create_operators(transforms, global_config)
save_visual_path = config['Global'].get('save_visual', "infer_result/")
if not os.path.exists(os.path.dirname(save_visual_path)):
os.makedirs(os.path.dirname(save_visual_path))
model.eval()
for file in get_image_file_list(config['Global']['infer_img']):
logger.info("infer_img: {}".format(file))
img = Image.open(file).convert("RGB")
data = {'image_lr': img}
batch = transform(data, ops)
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
preds = model(images)
sr_img = preds["sr_img"][0]
lr_img = preds["lr_img"][0]
fm_sr = (sr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8)
fm_lr = (lr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8)
img_name_pure = os.path.split(file)[-1]
cv2.imwrite("{}/sr_{}".format(save_visual_path, img_name_pure),
fm_sr[:, :, ::-1])
logger.info("The visualized image saved in infer_result/sr_{}".format(
img_name_pure))
logger.info("success!")
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main()
|